Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Clique Number Estimation via Differentiable Functions of Adjacency Matrix Permutations
Authors: Indradyumna Roy, Eeshaan Jain, Soumen Chakrabarti, Abir De
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on eight datasets show the superior accuracy of our approach. The code is available on Git Hub. ... 4 EXPERIMENTS We report on extensive experiments using eight datasets, comparing the performance of MXNET with other methods. We also instrument different components of MXNET to understand their impact. |
| Researcher Affiliation | Academia | Indradyumna Roy 1, Eeshaan Jain 2, Soumen Chakrabarti1, Abir De1 1IIT Bombay, 2EPFL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 MSS(B) # B is binary |
| Open Source Code | Yes | The code is available on Git Hub. |
| Open Datasets | Yes | Datasets We conduct experiments on eight datasets, comprising five real-world and three synthetic datasets. Real-world datasets include (1) IMDB-BINARY (IMDB), (2) Enzymes and modular products of graph pairs from (3) PTC-MM-m, (4) AIDS, (5) Mutagenicity (MUTAG-m) datasets. We also generate three synthetic datasets from (6) DSJC, (7) Brockington (Brock), and (8) RB. ... We use modular graph products for three datasets, viz., AIDS, MUTAG, PTC-MM. We call them AIDS-m, MUTAG-m and PTC-MM-m respectively. Additional details are in in Appendix E. ... sourced from the TUDatasets repository (Morris et al., 2020): (3) PTC-MM, (4) AIDS and (5) Mutagenicity. |
| Dataset Splits | Yes | We split each dataset D = {Gi, ω(Gi) | i [I]} into 60% training, 20% eval, and 20% test folds. |
| Hardware Specification | Yes | The training of our models and the baselines was performed on servers containing AMD EPYC 7642 48-Core Processors at 2.30GHz CPUs, and Nvidia RTX A6000 GPUs. |
| Software Dependencies | Yes | We implement our models using Python 3.10 and Py Torch 2.3.0. |
| Experiment Setup | Yes | All models are trained using the Adam optimizer, with a learning rate of 10 3, and weight decay 5 10 4. ... For the design of LComposite, we set δ = 1, and search for γ, λ in {0.25, 0.75}, and {0.1, 1} respectively. Hence, the total search space for hyperparameters consists of 8 combinations ({τ} {γ} {λ}), and we select the best model out of the 8 hyperparameter configurations. ... For the early stopping criteria based on the validation MSE, we use a patience parameter as 200 epochs. |